Overview

Dataset statistics

Number of variables31
Number of observations9082
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory248.0 B

Variable types

Categorical4
Numeric12
Boolean15

Warnings

Provider Name has a high cardinality: 212 distinct values High cardinality
Provider City has a high cardinality: 100 distinct values High cardinality
Number of All Beds is highly correlated with Total Number of Occupied BedsHigh correlation
Total Number of Occupied Beds is highly correlated with Number of All BedsHigh correlation
One-Week Supply of Surgical Masks is highly correlated with Any Current Supply of Eye Protection and 2 other fieldsHigh correlation
Any Current Supply of Eye Protection is highly correlated with One-Week Supply of Surgical Masks and 3 other fieldsHigh correlation
Any Current Supply of N95 Masks is highly correlated with Any Current Supply of Eye Protection and 2 other fieldsHigh correlation
Any Current Supply of Surgical Masks is highly correlated with One-Week Supply of Surgical Masks and 3 other fieldsHigh correlation
One-Week Supply of Eye Protection is highly correlated with One-Week Supply of Surgical Masks and 3 other fieldsHigh correlation
Week Ending is uniformly distributed Uniform
Provider Name is uniformly distributed Uniform
Residents Total Admissions COVID-19 has 1980 (21.8%) zeros Zeros
Residents Total Confirmed COVID-19 has 1362 (15.0%) zeros Zeros
Residents Total Suspected COVID-19 has 2621 (28.9%) zeros Zeros
Residents Weekly All Deaths has 5784 (63.7%) zeros Zeros
Residents Total All Deaths has 361 (4.0%) zeros Zeros
Residents Total COVID-19 Deaths has 2087 (23.0%) zeros Zeros
Staff Weekly Confirmed COVID-19 has 6708 (73.9%) zeros Zeros
Staff Total Confirmed COVID-19 has 827 (9.1%) zeros Zeros
Staff Weekly Suspected COVID-19 has 8549 (94.1%) zeros Zeros
Staff Total Suspected COVID-19 has 2797 (30.8%) zeros Zeros

Reproduction

Analysis started2021-04-26 13:56:02.415633
Analysis finished2021-04-26 13:57:03.098711
Duration1 minute and 0.68 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Week Ending
Categorical

UNIFORM

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size71.1 KiB
2020-07-12T00:00:00
 
212
2020-06-14T00:00:00
 
212
2020-06-07T00:00:00
 
212
2020-05-31T00:00:00
 
212
2020-07-26T00:00:00
 
212
Other values (38)
8022 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters172558
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-05-24T00:00:00
2nd row2020-05-24T00:00:00
3rd row2020-07-19T00:00:00
4th row2020-06-28T00:00:00
5th row2020-09-13T00:00:00
ValueCountFrequency (%)
2020-07-12T00:00:00212
 
2.3%
2020-06-14T00:00:00212
 
2.3%
2020-06-07T00:00:00212
 
2.3%
2020-05-31T00:00:00212
 
2.3%
2020-07-26T00:00:00212
 
2.3%
2020-09-06T00:00:00212
 
2.3%
2020-08-09T00:00:00212
 
2.3%
2020-08-30T00:00:00212
 
2.3%
2020-07-19T00:00:00212
 
2.3%
2020-06-21T00:00:00212
 
2.3%
Other values (33)6962
76.7%
2021-04-26T14:57:03.793361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-07-26t00:00:00212
 
2.3%
2020-07-12t00:00:00212
 
2.3%
2020-06-28t00:00:00212
 
2.3%
2020-08-16t00:00:00212
 
2.3%
2020-07-05t00:00:00212
 
2.3%
2020-05-24t00:00:00212
 
2.3%
2020-09-20t00:00:00212
 
2.3%
2020-09-06t00:00:00212
 
2.3%
2020-08-23t00:00:00212
 
2.3%
2020-07-19t00:00:00212
 
2.3%
Other values (33)6962
76.7%

Most occurring characters

ValueCountFrequency (%)
080908
46.9%
223652
 
13.7%
-18164
 
10.5%
:18164
 
10.5%
111170
 
6.5%
T9082
 
5.3%
72113
 
1.2%
81904
 
1.1%
31900
 
1.1%
61695
 
1.0%
Other values (3)3806
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127148
73.7%
Dash Punctuation18164
 
10.5%
Other Punctuation18164
 
10.5%
Uppercase Letter9082
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
080908
63.6%
223652
 
18.6%
111170
 
8.8%
72113
 
1.7%
81904
 
1.5%
31900
 
1.5%
61695
 
1.3%
91483
 
1.2%
41265
 
1.0%
51058
 
0.8%
ValueCountFrequency (%)
-18164
100.0%
ValueCountFrequency (%)
T9082
100.0%
ValueCountFrequency (%)
:18164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common163476
94.7%
Latin9082
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
080908
49.5%
223652
 
14.5%
-18164
 
11.1%
:18164
 
11.1%
111170
 
6.8%
72113
 
1.3%
81904
 
1.2%
31900
 
1.2%
61695
 
1.0%
91483
 
0.9%
Other values (2)2323
 
1.4%
ValueCountFrequency (%)
T9082
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII172558
100.0%

Most frequent character per block

ValueCountFrequency (%)
080908
46.9%
223652
 
13.7%
-18164
 
10.5%
:18164
 
10.5%
111170
 
6.5%
T9082
 
5.3%
72113
 
1.2%
81904
 
1.1%
31900
 
1.1%
61695
 
1.0%
Other values (3)3806
 
2.2%

Provider Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct212
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size71.1 KiB
NOBLE HORIZONS
 
43
TOUCHPOINTS AT FARMINGTON
 
43
MANCHESTER MANOR
 
43
GROTON REGENCY CENTER
 
43
MARLBOROUGH HEALTH & REHABILITATION CENTER
 
43
Other values (207)
8867 

Length

Max length50
Median length26
Mean length27.02554503
Min length7

Characters and Unicode

Total characters245446
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTHBRIDGE HEALTH CARE CENTER
2nd rowORCHARD GROVE SPECIALTY CARE CENTER, LLC
3rd rowWATROUS NURSING CENTER
4th rowAPPLE REHAB SHELTON LAKES
5th rowREGALCARE AT GREENWICH
ValueCountFrequency (%)
NOBLE HORIZONS43
 
0.5%
TOUCHPOINTS AT FARMINGTON43
 
0.5%
MANCHESTER MANOR43
 
0.5%
GROTON REGENCY CENTER43
 
0.5%
MARLBOROUGH HEALTH & REHABILITATION CENTER43
 
0.5%
LITCHFIELD WOODS HEALTH CARE C43
 
0.5%
GLASTONBURY HEALTH CARE CENTER43
 
0.5%
CONNECTICUT BAPTIST HOMES, INC43
 
0.5%
JEFFERSON HOUSE43
 
0.5%
MIDDLEBURY CONVALESCENT HOME43
 
0.5%
Other values (202)8652
95.3%
2021-04-26T14:57:04.319165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
center3354
 
9.2%
health2580
 
7.1%
care2064
 
5.7%
rehabilitation1591
 
4.4%
1462
 
4.0%
rehab1204
 
3.3%
at1118
 
3.1%
nursing1032
 
2.8%
manor721
 
2.0%
and688
 
1.9%
Other values (291)20668
56.7%

Most occurring characters

ValueCountFrequency (%)
E29588
12.1%
27400
11.2%
A21566
 
8.8%
R19688
 
8.0%
T17778
 
7.2%
N16320
 
6.6%
H14166
 
5.8%
I13888
 
5.7%
L13545
 
5.5%
O12245
 
5.0%
Other values (26)59262
24.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter214778
87.5%
Space Separator27400
 
11.2%
Other Punctuation2795
 
1.1%
Decimal Number258
 
0.1%
Dash Punctuation172
 
0.1%
Final Punctuation43
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
E29588
13.8%
A21566
10.0%
R19688
9.2%
T17778
8.3%
N16320
 
7.6%
H14166
 
6.6%
I13888
 
6.5%
L13545
 
6.3%
O12245
 
5.7%
C11739
 
5.5%
Other values (16)44255
20.6%
ValueCountFrequency (%)
&1462
52.3%
,1118
40.0%
'129
 
4.6%
.86
 
3.1%
ValueCountFrequency (%)
0129
50.0%
386
33.3%
643
 
16.7%
ValueCountFrequency (%)
27400
100.0%
ValueCountFrequency (%)
-172
100.0%
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin214778
87.5%
Common30668
 
12.5%

Most frequent character per script

ValueCountFrequency (%)
E29588
13.8%
A21566
10.0%
R19688
9.2%
T17778
8.3%
N16320
 
7.6%
H14166
 
6.6%
I13888
 
6.5%
L13545
 
6.3%
O12245
 
5.7%
C11739
 
5.5%
Other values (16)44255
20.6%
ValueCountFrequency (%)
27400
89.3%
&1462
 
4.8%
,1118
 
3.6%
-172
 
0.6%
'129
 
0.4%
0129
 
0.4%
386
 
0.3%
.86
 
0.3%
643
 
0.1%
43
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII245403
> 99.9%
Punctuation43
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
E29588
12.1%
27400
11.2%
A21566
 
8.8%
R19688
 
8.0%
T17778
 
7.2%
N16320
 
6.7%
H14166
 
5.8%
I13888
 
5.7%
L13545
 
5.5%
O12245
 
5.0%
Other values (25)59219
24.1%
ValueCountFrequency (%)
43
100.0%

Provider City
Categorical

HIGH CARDINALITY

Distinct100
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size71.1 KiB
WATERBURY
 
334
MERIDEN
 
301
WEST HARTFORD
 
215
DANBURY
 
215
NEW HAVEN
 
215
Other values (95)
7802 

Length

Max length16
Median length9
Mean length8.759083902
Min length4

Characters and Unicode

Total characters79550
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRIDGEPORT
2nd rowUNCASVILLE
3rd rowMADISON
4th rowSHELTON
5th rowGREENWICH
ValueCountFrequency (%)
WATERBURY334
 
3.7%
MERIDEN301
 
3.3%
WEST HARTFORD215
 
2.4%
DANBURY215
 
2.4%
NEW HAVEN215
 
2.4%
STAMFORD215
 
2.4%
NEW BRITAIN215
 
2.4%
WALLINGFORD172
 
1.9%
BRISTOL172
 
1.9%
TORRINGTON172
 
1.9%
Other values (90)6856
75.5%
2021-04-26T14:57:04.896474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new645
 
6.0%
hartford430
 
4.0%
haven430
 
4.0%
west344
 
3.2%
waterbury334
 
3.1%
meriden301
 
2.8%
windsor258
 
2.4%
britain215
 
2.0%
east215
 
2.0%
danbury215
 
2.0%
Other values (88)7329
68.4%

Most occurring characters

ValueCountFrequency (%)
R7524
 
9.5%
N6727
 
8.5%
E6526
 
8.2%
O6383
 
8.0%
T5537
 
7.0%
A5107
 
6.4%
I5093
 
6.4%
L4859
 
6.1%
D4386
 
5.5%
S3655
 
4.6%
Other values (14)23753
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter77916
97.9%
Space Separator1634
 
2.1%

Most frequent character per category

ValueCountFrequency (%)
R7524
 
9.7%
N6727
 
8.6%
E6526
 
8.4%
O6383
 
8.2%
T5537
 
7.1%
A5107
 
6.6%
I5093
 
6.5%
L4859
 
6.2%
D4386
 
5.6%
S3655
 
4.7%
Other values (13)22119
28.4%
ValueCountFrequency (%)
1634
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin77916
97.9%
Common1634
 
2.1%

Most frequent character per script

ValueCountFrequency (%)
R7524
 
9.7%
N6727
 
8.6%
E6526
 
8.4%
O6383
 
8.2%
T5537
 
7.1%
A5107
 
6.6%
I5093
 
6.5%
L4859
 
6.2%
D4386
 
5.6%
S3655
 
4.7%
Other values (13)22119
28.4%
ValueCountFrequency (%)
1634
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII79550
100.0%

Most frequent character per block

ValueCountFrequency (%)
R7524
 
9.5%
N6727
 
8.5%
E6526
 
8.2%
O6383
 
8.0%
T5537
 
7.0%
A5107
 
6.4%
I5093
 
6.4%
L4859
 
6.1%
D4386
 
5.5%
S3655
 
4.6%
Other values (14)23753
29.9%

Residents Total Admissions COVID-19
Real number (ℝ≥0)

ZEROS

Distinct196
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.21327901
Minimum0
Maximum535
Zeros1980
Zeros (%)21.8%
Memory size71.1 KiB
2021-04-26T14:57:05.103729image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q317
95-th percentile56
Maximum535
Range535
Interquartile range (IQR)16

Descriptive statistics

Standard deviation31.65158627
Coefficient of variation (CV)2.080523617
Kurtosis74.6270188
Mean15.21327901
Median Absolute Deviation (MAD)6
Skewness6.898305587
Sum138167
Variance1001.822914
MonotocityNot monotonic
2021-04-26T14:57:05.354707image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01980
21.8%
1902
 
9.9%
2491
 
5.4%
4461
 
5.1%
7373
 
4.1%
10357
 
3.9%
6332
 
3.7%
5310
 
3.4%
3296
 
3.3%
8271
 
3.0%
Other values (186)3309
36.4%
ValueCountFrequency (%)
01980
21.8%
1902
9.9%
2491
 
5.4%
3296
 
3.3%
4461
 
5.1%
ValueCountFrequency (%)
5351
< 0.1%
5281
< 0.1%
5211
< 0.1%
5131
< 0.1%
4941
< 0.1%

Residents Total Confirmed COVID-19
Real number (ℝ≥0)

ZEROS

Distinct159
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.77659106
Minimum0
Maximum171
Zeros1362
Zeros (%)15.0%
Memory size71.1 KiB
2021-04-26T14:57:05.631074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median28
Q363
95-th percentile102
Maximum171
Range171
Interquartile range (IQR)59

Descriptive statistics

Standard deviation35.79671296
Coefficient of variation (CV)0.9475898156
Kurtosis0.06923640367
Mean37.77659106
Median Absolute Deviation (MAD)27
Skewness0.8418299912
Sum343087
Variance1281.404658
MonotocityNot monotonic
2021-04-26T14:57:05.854750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01362
 
15.0%
1399
 
4.4%
2235
 
2.6%
3197
 
2.2%
5190
 
2.1%
18165
 
1.8%
28163
 
1.8%
20161
 
1.8%
23144
 
1.6%
17132
 
1.5%
Other values (149)5934
65.3%
ValueCountFrequency (%)
01362
15.0%
1399
 
4.4%
2235
 
2.6%
3197
 
2.2%
4107
 
1.2%
ValueCountFrequency (%)
1712
 
< 0.1%
17015
0.2%
1691
 
< 0.1%
1681
 
< 0.1%
1671
 
< 0.1%

Residents Total Suspected COVID-19
Real number (ℝ≥0)

ZEROS

Distinct107
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.43944065
Minimum0
Maximum293
Zeros2621
Zeros (%)28.9%
Memory size71.1 KiB
2021-04-26T14:57:06.096809image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q318
95-th percentile69
Maximum293
Range293
Interquartile range (IQR)18

Descriptive statistics

Standard deviation32.80970818
Coefficient of variation (CV)2.125058085
Kurtosis30.82677992
Mean15.43944065
Median Absolute Deviation (MAD)3
Skewness4.812728346
Sum140221
Variance1076.476951
MonotocityNot monotonic
2021-04-26T14:57:06.427745image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02621
28.9%
11071
 
11.8%
2598
 
6.6%
3517
 
5.7%
4287
 
3.2%
6272
 
3.0%
5213
 
2.3%
20200
 
2.2%
7182
 
2.0%
21178
 
2.0%
Other values (97)2943
32.4%
ValueCountFrequency (%)
02621
28.9%
11071
11.8%
2598
 
6.6%
3517
 
5.7%
4287
 
3.2%
ValueCountFrequency (%)
29325
0.3%
29212
0.1%
2881
 
< 0.1%
2861
 
< 0.1%
2791
 
< 0.1%

Residents Weekly All Deaths
Real number (ℝ≥0)

ZEROS

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8313146884
Minimum0
Maximum71
Zeros5784
Zeros (%)63.7%
Memory size71.1 KiB
2021-04-26T14:57:06.804881image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum71
Range71
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.777344239
Coefficient of variation (CV)3.34090601
Kurtosis175.2695731
Mean0.8313146884
Median Absolute Deviation (MAD)0
Skewness11.04748687
Sum7550
Variance7.71364102
MonotocityNot monotonic
2021-04-26T14:57:07.073736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
05784
63.7%
12033
 
22.4%
2724
 
8.0%
3223
 
2.5%
4104
 
1.1%
534
 
0.4%
624
 
0.3%
724
 
0.3%
814
 
0.2%
1013
 
0.1%
Other values (34)105
 
1.2%
ValueCountFrequency (%)
05784
63.7%
12033
 
22.4%
2724
 
8.0%
3223
 
2.5%
4104
 
1.1%
ValueCountFrequency (%)
711
< 0.1%
661
< 0.1%
561
< 0.1%
521
< 0.1%
511
< 0.1%

Residents Total All Deaths
Real number (ℝ≥0)

ZEROS

Distinct138
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.08852676
Minimum0
Maximum196
Zeros361
Zeros (%)4.0%
Memory size71.1 KiB
2021-04-26T14:57:07.310398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median19
Q332
95-th percentile61
Maximum196
Range196
Interquartile range (IQR)23

Descriptive statistics

Standard deviation20.54581936
Coefficient of variation (CV)0.8898713883
Kurtosis7.830760268
Mean23.08852676
Median Absolute Deviation (MAD)11
Skewness2.063673226
Sum209690
Variance422.1306931
MonotocityNot monotonic
2021-04-26T14:57:07.614768image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0361
 
4.0%
1343
 
3.8%
19299
 
3.3%
3294
 
3.2%
15254
 
2.8%
10248
 
2.7%
2238
 
2.6%
21237
 
2.6%
9230
 
2.5%
11229
 
2.5%
Other values (128)6349
69.9%
ValueCountFrequency (%)
0361
4.0%
1343
3.8%
2238
2.6%
3294
3.2%
4220
2.4%
ValueCountFrequency (%)
1961
< 0.1%
1921
< 0.1%
1912
< 0.1%
1861
< 0.1%
1851
< 0.1%

Residents Total COVID-19 Deaths
Real number (ℝ≥0)

ZEROS

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.08797622
Minimum0
Maximum84
Zeros2087
Zeros (%)23.0%
Memory size71.1 KiB
2021-04-26T14:57:07.866039image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q316
95-th percentile29
Maximum84
Range84
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.92314658
Coefficient of variation (CV)1.082788693
Kurtosis5.562729679
Mean10.08797622
Median Absolute Deviation (MAD)7
Skewness1.81117525
Sum91619
Variance119.3151313
MonotocityNot monotonic
2021-04-26T14:57:08.077515image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02087
23.0%
1560
 
6.2%
7498
 
5.5%
2365
 
4.0%
5359
 
4.0%
6349
 
3.8%
13342
 
3.8%
4328
 
3.6%
9326
 
3.6%
8299
 
3.3%
Other values (55)3569
39.3%
ValueCountFrequency (%)
02087
23.0%
1560
 
6.2%
2365
 
4.0%
3172
 
1.9%
4328
 
3.6%
ValueCountFrequency (%)
845
0.1%
833
< 0.1%
813
< 0.1%
782
 
< 0.1%
761
 
< 0.1%

Number of All Beds
Real number (ℝ≥0)

HIGH CORRELATION

Distinct92
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.314248
Minimum0
Maximum360
Zeros3
Zeros (%)< 0.1%
Memory size71.1 KiB
2021-04-26T14:57:08.350382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45
Q176
median120
Q3137
95-th percentile217
Maximum360
Range360
Interquartile range (IQR)61

Descriptive statistics

Standard deviation55.49257174
Coefficient of variation (CV)0.4770917812
Kurtosis4.211328111
Mean116.314248
Median Absolute Deviation (MAD)30
Skewness1.533746068
Sum1056366
Variance3079.425519
MonotocityNot monotonic
2021-04-26T14:57:08.609513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1201328
 
14.6%
60798
 
8.8%
90588
 
6.5%
150487
 
5.4%
130405
 
4.5%
75258
 
2.8%
128215
 
2.4%
160215
 
2.4%
180172
 
1.9%
126172
 
1.9%
Other values (82)4444
48.9%
ValueCountFrequency (%)
03
 
< 0.1%
2343
0.5%
2543
0.5%
3086
0.9%
3586
0.9%
ValueCountFrequency (%)
36043
0.5%
35743
0.5%
34543
0.5%
29443
0.5%
28243
0.5%

Total Number of Occupied Beds
Real number (ℝ≥0)

HIGH CORRELATION

Distinct277
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.05450341
Minimum0
Maximum901
Zeros34
Zeros (%)0.4%
Memory size71.1 KiB
2021-04-26T14:57:08.867764image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q156
median81
Q3104
95-th percentile159
Maximum901
Range901
Interquartile range (IQR)48

Descriptive statistics

Standard deviation42.85129212
Coefficient of variation (CV)0.5038097973
Kurtosis18.40884244
Mean85.05450341
Median Absolute Deviation (MAD)24
Skewness2.139015977
Sum772465
Variance1836.233237
MonotocityNot monotonic
2021-04-26T14:57:09.123967image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81184
 
2.0%
77136
 
1.5%
78135
 
1.5%
82131
 
1.4%
83131
 
1.4%
80125
 
1.4%
84124
 
1.4%
79124
 
1.4%
75120
 
1.3%
85117
 
1.3%
Other values (267)7755
85.4%
ValueCountFrequency (%)
034
0.4%
14
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
ValueCountFrequency (%)
9011
< 0.1%
3231
< 0.1%
3171
< 0.1%
3142
< 0.1%
3131
< 0.1%

Staff Weekly Confirmed COVID-19
Real number (ℝ≥0)

ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8215150848
Minimum0
Maximum75
Zeros6708
Zeros (%)73.9%
Memory size71.1 KiB
2021-04-26T14:57:09.357991image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum75
Range75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.144041826
Coefficient of variation (CV)3.827126104
Kurtosis132.8126923
Mean0.8215150848
Median Absolute Deviation (MAD)0
Skewness9.726490451
Sum7461
Variance9.884999004
MonotocityNot monotonic
2021-04-26T14:57:09.725252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
06708
73.9%
11251
 
13.8%
2456
 
5.0%
3237
 
2.6%
498
 
1.1%
655
 
0.6%
551
 
0.6%
736
 
0.4%
831
 
0.3%
919
 
0.2%
Other values (37)140
 
1.5%
ValueCountFrequency (%)
06708
73.9%
11251
 
13.8%
2456
 
5.0%
3237
 
2.6%
498
 
1.1%
ValueCountFrequency (%)
751
< 0.1%
591
< 0.1%
562
< 0.1%
521
< 0.1%
511
< 0.1%

Staff Total Confirmed COVID-19
Real number (ℝ≥0)

ZEROS

Distinct118
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.67022682
Minimum0
Maximum130
Zeros827
Zeros (%)9.1%
Memory size71.1 KiB
2021-04-26T14:57:09.959270image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median15
Q331
95-th percentile60
Maximum130
Range130
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.46763429
Coefficient of variation (CV)0.990198824
Kurtosis3.471336276
Mean20.67022682
Median Absolute Deviation (MAD)12
Skewness1.597035582
Sum187727
Variance418.9240534
MonotocityNot monotonic
2021-04-26T14:57:10.261696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0827
 
9.1%
1536
 
5.9%
2378
 
4.2%
3329
 
3.6%
5281
 
3.1%
4274
 
3.0%
8272
 
3.0%
23220
 
2.4%
14218
 
2.4%
15216
 
2.4%
Other values (108)5531
60.9%
ValueCountFrequency (%)
0827
9.1%
1536
5.9%
2378
4.2%
3329
 
3.6%
4274
 
3.0%
ValueCountFrequency (%)
1305
0.1%
1261
 
< 0.1%
1231
 
< 0.1%
1177
0.1%
11611
0.1%

Staff Weekly Suspected COVID-19
Real number (ℝ≥0)

ZEROS

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2176833297
Minimum0
Maximum53
Zeros8549
Zeros (%)94.1%
Memory size71.1 KiB
2021-04-26T14:57:10.517319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum53
Range53
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.908507433
Coefficient of variation (CV)8.767356859
Kurtosis336.3682511
Mean0.2176833297
Median Absolute Deviation (MAD)0
Skewness16.69552629
Sum1977
Variance3.642400623
MonotocityNot monotonic
2021-04-26T14:57:10.784766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
08549
94.1%
1284
 
3.1%
2108
 
1.2%
341
 
0.5%
422
 
0.2%
510
 
0.1%
69
 
0.1%
87
 
0.1%
105
 
0.1%
74
 
< 0.1%
Other values (26)43
 
0.5%
ValueCountFrequency (%)
08549
94.1%
1284
 
3.1%
2108
 
1.2%
341
 
0.5%
422
 
0.2%
ValueCountFrequency (%)
532
< 0.1%
481
< 0.1%
421
< 0.1%
411
< 0.1%
391
< 0.1%

Staff Total Suspected COVID-19
Real number (ℝ≥0)

ZEROS

Distinct68
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.109997798
Minimum0
Maximum80
Zeros2797
Zeros (%)30.8%
Memory size71.1 KiB
2021-04-26T14:57:11.026599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile36
Maximum80
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.26590612
Coefficient of variation (CV)1.635747191
Kurtosis6.024255523
Mean8.109997798
Median Absolute Deviation (MAD)2
Skewness2.412339573
Sum73655
Variance175.9842652
MonotocityNot monotonic
2021-04-26T14:57:11.269512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02797
30.8%
11051
 
11.6%
2879
 
9.7%
3632
 
7.0%
5330
 
3.6%
10271
 
3.0%
4268
 
3.0%
6211
 
2.3%
8205
 
2.3%
9201
 
2.2%
Other values (58)2237
24.6%
ValueCountFrequency (%)
02797
30.8%
11051
 
11.6%
2879
 
9.7%
3632
 
7.0%
4268
 
3.0%
ValueCountFrequency (%)
8017
0.2%
6817
0.2%
6619
0.2%
651
 
< 0.1%
6318
0.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.1 KiB
0
8505 
1
 
534
2
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9082
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
08505
93.6%
1534
 
5.9%
243
 
0.5%
2021-04-26T14:57:11.700981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-26T14:57:11.822352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
08505
93.6%
1534
 
5.9%
243
 
0.5%

Most occurring characters

ValueCountFrequency (%)
08505
93.6%
1534
 
5.9%
243
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9082
100.0%

Most frequent character per category

ValueCountFrequency (%)
08505
93.6%
1534
 
5.9%
243
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common9082
100.0%

Most frequent character per script

ValueCountFrequency (%)
08505
93.6%
1534
 
5.9%
243
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII9082
100.0%

Most frequent character per block

ValueCountFrequency (%)
08505
93.6%
1534
 
5.9%
243
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
False
8574 
True
 
508
ValueCountFrequency (%)
False8574
94.4%
True508
 
5.6%
2021-04-26T14:57:11.894853image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
False
8880 
True
 
202
ValueCountFrequency (%)
False8880
97.8%
True202
 
2.2%
2021-04-26T14:57:11.961218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
False
8531 
True
 
551
ValueCountFrequency (%)
False8531
93.9%
True551
 
6.1%
2021-04-26T14:57:12.025082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
False
8802 
True
 
280
ValueCountFrequency (%)
False8802
96.9%
True280
 
3.1%
2021-04-26T14:57:12.699922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Any Current Supply of N95 Masks
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
8670 
False
 
412
ValueCountFrequency (%)
True8670
95.5%
False412
 
4.5%
2021-04-26T14:57:12.778381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
7797 
False
1285 
ValueCountFrequency (%)
True7797
85.9%
False1285
 
14.1%
2021-04-26T14:57:12.855090image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
8681 
False
 
401
ValueCountFrequency (%)
True8681
95.6%
False401
 
4.4%
2021-04-26T14:57:12.924615image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

One-Week Supply of Surgical Masks
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
8618 
False
 
464
ValueCountFrequency (%)
True8618
94.9%
False464
 
5.1%
2021-04-26T14:57:13.000981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
8682 
False
 
400
ValueCountFrequency (%)
True8682
95.6%
False400
 
4.4%
2021-04-26T14:57:13.067912image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

One-Week Supply of Eye Protection
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
8638 
False
 
444
ValueCountFrequency (%)
True8638
95.1%
False444
 
4.9%
2021-04-26T14:57:13.144304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
8380 
False
 
702
ValueCountFrequency (%)
True8380
92.3%
False702
 
7.7%
2021-04-26T14:57:13.227076image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
9025 
False
 
57
ValueCountFrequency (%)
True9025
99.4%
False57
 
0.6%
2021-04-26T14:57:13.332427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
False
8519 
True
 
563
ValueCountFrequency (%)
False8519
93.8%
True563
 
6.2%
2021-04-26T14:57:13.398141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
9060 
False
 
22
ValueCountFrequency (%)
True9060
99.8%
False22
 
0.2%
2021-04-26T14:57:13.466285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
True
9047 
False
 
35
ValueCountFrequency (%)
True9047
99.6%
False35
 
0.4%
2021-04-26T14:57:13.547862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Interactions

2021-04-26T14:56:14.856215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:15.162968image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:15.804521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:17.684235image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:17.989586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:18.270200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:18.985828image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:19.345663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:20.625481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:21.267642image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:21.954161image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:22.250827image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:22.847782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:23.046311image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:23.321318image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:23.579960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:23.771949image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:23.982698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:24.204644image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:26.708812image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:27.940163image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:28.698411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:28.912362image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:29.231261image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:29.626578image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:29.835427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:30.447939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:30.712958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:30.923923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:31.217454image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:31.633092image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:31.828472image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:32.029259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:32.276930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:32.468511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:32.731717image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:32.926586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:33.133510image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:33.746182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:33.963271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:34.159602image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:34.560150image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:34.795018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:34.982179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:35.207982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:35.436611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:35.742399image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:35.946731image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:36.166733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:36.415393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:36.684894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:36.901967image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:37.339478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:37.580615image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:37.826051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:38.041708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:38.253878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:38.484248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:38.709460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:38.892590image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:39.070632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:39.279462image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:39.469800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:39.844999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:40.025214image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:40.209794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:40.439990image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:40.689787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:40.880099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:41.074491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:41.297617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:41.542367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:41.807987image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:42.006117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:42.378826image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:42.636897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:42.833269image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:43.028381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:43.220757image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:43.436140image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:43.985652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:44.180266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:44.410716image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:44.625355image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:44.810189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:45.140019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:45.349520image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:45.521989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:45.764531image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:46.224918image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:46.748876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:47.217673image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:47.814616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:48.236732image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:48.750797image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:49.159513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:49.638526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:50.034228image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:50.675077image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:51.130864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:51.395269image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:51.702086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:51.938841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:52.142648image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:52.356143image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:52.591973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:52.788221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:52.971191image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:53.366441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:53.557472image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:53.809581image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:53.988565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:54.194546image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:54.405756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:54.634624image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:54.832172image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:55.008699image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:55.203008image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:55.444030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:55.925729image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:56.129374image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:56.376787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:56.674037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:56.911516image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:57.194588image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:57.930748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:58.155156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:58.380559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:58.647972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:58.850622image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:59.214861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T14:56:59.565612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-26T14:57:13.715938image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-26T14:57:14.223345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-26T14:57:14.631288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-26T14:57:15.052935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-26T14:57:15.785251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-26T14:57:00.208599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-26T14:57:02.382763image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Week EndingProvider NameProvider CityResidents Total Admissions COVID-19Residents Total Confirmed COVID-19Residents Total Suspected COVID-19Residents Weekly All DeathsResidents Total All DeathsResidents Total COVID-19 DeathsNumber of All BedsTotal Number of Occupied BedsStaff Weekly Confirmed COVID-19Staff Total Confirmed COVID-19Staff Weekly Suspected COVID-19Staff Total Suspected COVID-19Staff Total COVID-19 DeathsShortage of Nursing StaffShortage of Clinical StaffShortage of AidesShortage of Other StaffAny Current Supply of N95 MasksOne-Week Supply of N95 MasksAny Current Supply of Surgical MasksOne-Week Supply of Surgical MasksAny Current Supply of Eye ProtectionOne-Week Supply of Eye ProtectionOne-Week Supply of GownsAny Current Supply of Hand SanitizerThree or More Confirmed COVID-19 Cases This Week or Initial Confirmed COVID-19 Case this WeekAble to Test or Obtain Resources to Test All Current Residents Within Next 7 DaysAble to Test or Obtain Resources to Test All Staff and/or Personnel Within Next 7 Days
02020-05-24T00:00:00NORTHBRIDGE HEALTH CARE CENTERBRIDGEPORT25000000011000NNNNYYYYYYYYNYY
12020-05-24T00:00:00ORCHARD GROVE SPECIALTY CARE CENTER, LLCUNCASVILLE6283019712081015020NNNNYYYYYYYYNYY
22020-07-19T00:00:00WATROUS NURSING CENTERMADISON011000452901000NNNNYYYYYYYYNYY
32020-06-28T00:00:00APPLE REHAB SHELTON LAKESSHELTON1001201067300000NNNNYYYYYYYYNYY
42020-09-13T00:00:00REGALCARE AT GREENWICHGREENWICH1112002814756207000NNNNYYYYYYYYNYY
52020-11-15T00:00:00WITHERELL, NATHANIELGREENWICH161301401616202152111000NNNNYYYYYYYYNYY
62020-12-27T00:00:00AVERY NURSING HOMEHARTFORD25821139211991411920110NNNNYYYYYYYYYYY
72020-08-30T00:00:00REGALCARE AT GREENWICHGREENWICH1112002814756407000NNNNYYYYYYYYNYY
82020-10-11T00:00:00GROVE MANOR NURSING HOME, INCWATERBURY2290021604317000YYYYYYYYYYYYNYY
92021-01-31T00:00:00WINDSOR HEALTH AND REHABILITATION CENTER, LLCWINDSOR781034010888018000YNYYYYYYYYYYNYY

Last rows

Week EndingProvider NameProvider CityResidents Total Admissions COVID-19Residents Total Confirmed COVID-19Residents Total Suspected COVID-19Residents Weekly All DeathsResidents Total All DeathsResidents Total COVID-19 DeathsNumber of All BedsTotal Number of Occupied BedsStaff Weekly Confirmed COVID-19Staff Total Confirmed COVID-19Staff Weekly Suspected COVID-19Staff Total Suspected COVID-19Staff Total COVID-19 DeathsShortage of Nursing StaffShortage of Clinical StaffShortage of AidesShortage of Other StaffAny Current Supply of N95 MasksOne-Week Supply of N95 MasksAny Current Supply of Surgical MasksOne-Week Supply of Surgical MasksAny Current Supply of Eye ProtectionOne-Week Supply of Eye ProtectionOne-Week Supply of GownsAny Current Supply of Hand SanitizerThree or More Confirmed COVID-19 Cases This Week or Initial Confirmed COVID-19 Case this WeekAble to Test or Obtain Resources to Test All Current Residents Within Next 7 DaysAble to Test or Obtain Resources to Test All Staff and/or Personnel Within Next 7 Days
90722020-08-16T00:00:00SOUTHINGTON CARE CENTERSOUTHINGTON145912115913097031090NNNNYYYYYYYYNYY
90732021-01-17T00:00:00APPLE REHAB COLCHESTERCOLCHESTER04500426037033000NNNNYYYYYYYYNYY
90742020-07-12T00:00:00AVON HEALTH CENTERAVON550750327120920120320NNNNYYYYYYYYNYY
90752020-12-20T00:00:00TOUCHPOINTS AT BLOOMFIELDBLOOMFIELD641022020151501180210591NNNNYNYYYYYYNYY
90762020-11-01T00:00:00WATROUS NURSING CENTERMADISON011000452701000NNNNYYYYYYYYNYY
90772020-08-23T00:00:00NOTRE DAME CONVALESCENT HOME INORWALK322380171360450200130NNNNYYYYYYYYNYY
90782021-01-24T00:00:00LEDGECREST HEALTH CAREKENSINGTON626101555548012000NNNNYYYYYYYYNYY
90792021-01-31T00:00:00JOHN L. LEVITOW HEALTH CARE CENTERROCKY HILL12340020712581242030NNNNYYYYYYYYNYY
90802020-12-06T00:00:00APPLE REHAB SHELTON LAKESSHELTON821011001067924000NNNNYYYYYYYYYYY
90812020-08-23T00:00:00BETHEL HEALTH CARE CENTERBETHEL236166044161611190300191NNNNYYYYYYYYNYY